论文标题
Cyberwalle在Semeval-2020任务11：用于宣传检测合奏模型的功能工程分析
CyberWallE at SemEval-2020 Task 11: An Analysis of Feature Engineering for Ensemble Models for Propaganda Detection
论文作者
论文摘要
本文描述了我们参与新闻文章中宣传技术的Semeval-2020任务检测。我们参与两个子任务：跨度识别（SI）和技术分类（TC）。我们在SI子任务中使用BI-LSTM架构，并为TC子任务训练复杂的集合模型。我们的体系结构是使用Bert的嵌入以及其他词汇功能和广泛的标签后处理来构建的。我们的系统在SI子任务（F1得分：43.86％）中获得了35个团队中的8个，在TC子任务中，有31个团队中有8个队伍（F1-SCORE：57.37％）。
This paper describes our participation in the SemEval-2020 task Detection of Propaganda Techniques in News Articles. We participate in both subtasks: Span Identification (SI) and Technique Classification (TC). We use a bi-LSTM architecture in the SI subtask and train a complex ensemble model for the TC subtask. Our architectures are built using embeddings from BERT in combination with additional lexical features and extensive label post-processing. Our systems achieve a rank of 8 out of 35 teams in the SI subtask (F1-score: 43.86%) and 8 out of 31 teams in the TC subtask (F1-score: 57.37%).
